Most of the recent network architectures used for speaker embedding extraction are composed of a front-end feature extractor, a pooling layer, and a set of fc layers. Lately, there have been several architectures proposed to encode audio utterances into speaker embeddings for different choices of network inputs, such as [1]}, [2]}, [3]}, [4]}, [5]}. Using mfcc features, tdnn [1]}, [2]} is the most currently used architecture. tdnn is the x-vector front-end and consists of a stack of 1-D dilated cnn. The idea behind the use of tdnn is to encode a sequence of mfcc into a more discriminative sequence of vectors by capturing long-term feature relations. 2-D cnn have also shown competitive results for speaker verification. There are Computer Vision architectures such as VGG [3]}, [9]}, [10]} and ResNet [4]}, [12]}, [13]} that have been adapted to capture speaker discriminative information from the Mel-Spectrograms. In fact, Resnet34 has shown a better performance than tdnn in the most recent speaker verification challenges [14]}, [15]}. Finally, there are also some other attempts to work directly on the raw signal instead of using hand-crafted features [16]}, [17]}, [18]}.
